Schema Markup (Structured Data)

Schema markup (structured data) is standardized code added to web pages that helps search engines and AI systems understand content meaning and structure. Implemented as JSON-LD script tags in the page head, schema describes entities, relationships, and attributes in a machine-readable format defined by Schema.org.

For AI search visibility, schema markup serves three critical functions. First, it helps AI systems classify page content type — Article, Product, FAQ, HowTo, Organization — enabling more accurate matching between user queries and page content. Second, it provides verifiable metadata: author identity, publication dates, pricing, and review data that AI systems use for trust evaluation. Third, schema with content-match accuracy (where structured data accurately reflects actual page content) increases citation confidence.

The highest-impact schema types for AI citation are: Article schema with headline, author, datePublished, and dateModified (establishes content identity and freshness); FAQPage schema (AI systems extract FAQ answers almost verbatim — pages with FAQPage schema are cited approximately 2x more frequently for Q&A queries); Product schema with price, currency, and availability (critical for commercial queries expanding into AI Overviews); Organization schema with company details and social profiles (establishes entity credibility); and BreadcrumbList schema (helps AI understand site structure and content hierarchy).

JSON-LD is the recommended implementation format. Validate with Google's Rich Results Test for syntax correctness, then audit with a tool like TurboAudit for content-match accuracy — verifying that your schema data actually matches what's on the page.